דרושים Privacy-Preserving Machine Learning Researcher - Q בהרצליה | AllJobs

לפני 57 דקות

Deep Learning Research Engineer - ML & Physics - Q

חברה חסויה

Location: Herzliya

Job Type: Full Time

We are seeking a creative and hands-on Deep Learning Research Engineer to drive model and architecture innovation across our sensing platforms. In this role, you will go beyond off-the-shelf models, exploring novel neural network designs, designing rigorous training and validation pipelines, and translating inventive concepts into clean, high-performance implementations. While our domain deals with physical data, this is an ML-first role: we are looking for a deep learning specialist with strong research intuition, expertise in PyTorch, and the drive to solve non-standard, real-world problems.

Responsibilities
Conceive, design, and implement novel neural network architectures tailored for complex physical sensing, reconstruction, and inverse problems.
Lead end-to-end hands-on research: data preparation, model formulation, training, hyperparameter exploration, and failure-mode analysis.
Build, train, and rigorously validate deep learning models using Python and PyTorch.
Formulate custom loss functions, regularization techniques, and inductive biases that capture domain-specific constraints.
Design comprehensive validation and benchmarking suites to stress-test model robustness, generalization, and scaling behavior.
Propose creative, non-conventional modeling ideas, stay abreast of state-of-the-art DL literature, and rapidly prototype proof-of-concepts.
Collaborate cross-functionally with physicists and optical software engineers to benchmark models against simulated and experimental lab datasets.

Requirements:
Minimum Qualifications
M.Sc. in Computer Science, Electrical Engineering, Data Science, Applied Mathematics, Physics, or a related technical field.
3+ years of hands-on research experience in machine learning and deep learning (in industry R&D or an academic research lab).
Deep theoretical and practical understanding of modern deep learning fundamentals (e.g., neural network architectures, optimization landscapes, generalization, attention/transformers, CNNs, or implicit representations).
Proven hands-on proficiency in Python and PyTorch, including writing custom layers, loss functions, and efficient training loops.
Demonstrated track record of creative problem-solving, proposing novel ideas, and executing them from hypothesis to experimental validation.
Strong technical communication skills and fluency in English

Preferred Qualifications
Track record of research contributions (e.g., publications in top-tier conferences like CVPR, ICCV, NeurIPS, ICLR, ICML, or relevant patents).
Foundational understanding or interest in physics, wave optics, signal processing, or computational imaging.
Experience with physics-informed neural networks (PINNs), neural operators (FNO/DeepONet), or integrating physical differential equations into loss functions.
Experience with large-scale distributed training, model profiling, and GPU acceleration.

This position is open to all candidates.

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8892453

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29/10/2026

Senior Signal/Image Processing & Machine Learning Engineer - Sensing Technologies & Real-Time Models

חברה חסויה

Location: Herzliya and Haifa

Job Type: Full Time

We are seeking an experienced Signal/Image Processing and Machine Learning engineer to join our team. The role focuses on developing innovative, real-time deep learning and signal processing solutions for new sensing and imaging systems, including video restoration. You will be part of an interdisciplinary team developing technologies that will shape future Apple products.
Description
Algorithm and model development:
- Design, implement, and optimize signal processing and machine learning models for sensing and imaging tasks, including video restoration.
- Develop and validate models using real sensor data, simulated data, and public datasets.
- Prepare, filter, process, and analyze large-scale datasets to train and evaluate models.
- Analyze your algorithm's results and benchmark them against leading methods, and theoretical bounds.
- Optimize models for real-time performance, low latency, and resource efficiency on-device.

Sensing and research:
- Develop algorithms that combine classical signal processing with learned models to extract robust information from sensor data.
- Investigate efficient architecture patterns (lightweight backbones, attention mechanisms, recurrent designs) suitable for real-time, on-device inference.
- Explore how algorithmic approaches can mitigate artifacts and limitations of new sensing and imaging systems.
- Stay current with recent advances in signal processing, computer vision, and machine learning.

Collaboration and technical leadership:
- Work closely with ML researchers, SW, HW, sensor, and architecture engineers to translate research ideas into robust models and practical, shipping systems.
- Provide technical guidance and mentorship to junior engineers, and help shape the team's technical direction.

Responsibilities
- Own the end-to-end development of real-time algorithms, from concept and prototyping through validation and deployment.
- Define evaluation methodology, metrics, and failure analysis processes for sensing and imaging algorithms.
- Drive trade-off decisions among accuracy, latency, power, and memory across algorithm, HW, and system constraints.

Requirements:
Minimum Qualifications
Strong foundation in signal processing, image processing, computer vision, machine learning, and deep learning.
Hands-on experience with sensing technologies (e.g., camera/image sensors, depth, radar, LiDAR, IMU, or other sensor modalities), including an understanding of sensor noise models and characteristics.
Demonstrated experience designing and deploying real-time deep learning and signal processing solutions using large-scale datasets.
Proficiency in Python and deep learning frameworks (e.g., PyTorch); experience with C++ or other performance-oriented languages is a plus.
Experience with model evaluation, debugging, experimental analysis, and failure analysis.
PhD or Master's degree in Electrical Engineering, Computer Science, or a related research field.
Proficient in written and verbal communication.
Ability to work both autonomously and collaboratively.
8+ years of relevant experience.

Preferred Qualifications
Experience deploying models on GPU, dedicated HW, or neural engines, including quantization, pruning, and other model compression techniques.
Experience with multi-sensor fusion, sensor calibration, or temporal/streaming signal processing.
Experience with generative priors (diffusion, flow matching) is an advantage.
Background in signal processing, physics, optics, lasers is a plus.

This position is open to all candidates.

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8877020

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29/10/2026

Senior Signal/Image Processing & Machine Learning Engineer - Sensing Technologies & Real-Time Models

חברה חסויה

Location: Herzliya and Haifa

Job Type: Full Time

We are seeking an experienced Signal/Image Processing and Machine Learning engineer to join our team. The role focuses on developing innovative, real-time deep learning and signal processing solutions for new sensing and imaging systems, including video restoration. You will be part of an interdisciplinary team developing technologies that will shape future Apple products.
Description
Algorithm and model development:
- Design, implement, and optimize signal processing and machine learning models for sensing and imaging tasks, including video restoration.
- Develop and validate models using real sensor data, simulated data, and public datasets.
- Prepare, filter, process, and analyze large-scale datasets to train and evaluate models.
- Analyze your algorithm's results and benchmark them against leading methods, and theoretical bounds.
- Optimize models for real-time performance, low latency, and resource efficiency on-device.

Sensing and research:
- Develop algorithms that combine classical signal processing with learned models to extract robust information from sensor data.
- Investigate efficient architecture patterns (lightweight backbones, attention mechanisms, recurrent designs) suitable for real-time, on-device inference.
- Explore how algorithmic approaches can mitigate artifacts and limitations of new sensing and imaging systems.
- Stay current with recent advances in signal processing, computer vision, and machine learning.

Collaboration and technical leadership:
- Work closely with ML researchers, SW, HW, sensor, and architecture engineers to translate research ideas into robust models and practical, shipping systems.
- Provide technical guidance and mentorship to junior engineers, and help shape the team's technical direction.

Responsibilities
- Own the end-to-end development of real-time algorithms, from concept and prototyping through validation and deployment.
- Define evaluation methodology, metrics, and failure analysis processes for sensing and imaging algorithms.
- Drive trade-off decisions among accuracy, latency, power, and memory across algorithm, HW, and system constraints.

Requirements:
Minimum Qualifications
Strong foundation in signal processing, image processing, computer vision, machine learning, and deep learning.
Hands-on experience with sensing technologies (e.g., camera/image sensors, depth, radar, LiDAR, IMU, or other sensor modalities), including an understanding of sensor noise models and characteristics.
Demonstrated experience designing and deploying real-time deep learning and signal processing solutions using large-scale datasets.
Proficiency in Python and deep learning frameworks (e.g., PyTorch); experience with C++ or other performance-oriented languages is a plus.
Experience with model evaluation, debugging, experimental analysis, and failure analysis.
PhD or Master's degree in Electrical Engineering, Computer Science, or a related research field.
Proficient in written and verbal communication.
Ability to work both autonomously and collaboratively.
8+ years of relevant experience.

Preferred Qualifications
Experience deploying models on GPU, dedicated HW, or neural engines, including quantization, pruning, and other model compression techniques.
Experience with multi-sensor fusion, sensor calibration, or temporal/streaming signal processing.
Experience with generative priors (diffusion, flow matching) is an advantage.
Background in signal processing, physics, optics, lasers is a plus.

This position is open to all candidates.

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8877032

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29/10/2026

Machine Learning Ph.D Student - Agentic AI

חברה חסויה

Location: Herzliya

Job Type: Full Time

As a member of our ML group, you will design, prototype, and ship agentic GenAI systems used by chip design engineers to accelerate their day-to-day workflows. We are looking for a machine learning engineer who is energized by taking LLMs from "promising demo" to "depended-on tool": designing the agent loops, retrieval pipelines, and evaluation frameworks that make this transition real. Success in this role means agents that meaningfully shift how our customer teams work, backed by measurements you trust.

You will join a growing team of ML and software engineers building GenAI solutions for chip design teams. In this role, you will:

Design and build agentic systems featuring multi-agent coordination, long-horizon reasoning, tool use, and planning frameworks.
Develop end-to-end solutions, including domain-specific agents and high-performance retrieval pipelines spanning diverse data sources.
Partner with subject-matter experts on the customer teams to translate their workflows and rules of thumb into concrete agent assignments, prompts, tools, and guidelines.
Build the evaluation backbone for our agents: define success measures, curate offline golden sets, and instrument online telemetry to power feedback loops that drive fast, safe iteration with real cost and quality control.
Drive prompt engineering, model selection, and lightweight model adaptation as the workloads demand.
Collaborate closely with software engineers on the team to productionize your work, and with researchers and customers to shape what we build next.
Stay current with the rapidly evolving agentic and LLM landscape and bring promising techniques into our products with good engineering judgment.

Requirements:
Minimum Qualifications
MSc, or PhD in Computer Science, Computer Engineering, Electrical Engineering, or related fields.
Strong programming skills in Python and experience with modern ML and deep learning frameworks (e.g., PyTorch).
Hands-on experience with agentic frameworks, tool use, and data pipelines.
Strong evaluation mindset: comfortable designing golden sets, metrics, and telemetry to drive iteration.
Excellent communication and collaboration skills, with the ability to align with subject-matter experts and translate their domain knowledge into agent behavior.

Preferred Qualifications
Hands-on experience with stateful, multi-turn agentic frameworks and complex execution flows.
Experience with prompt engineering and optimization techniques.
Experience with model adaptation (e.g., fine-tuning, distillation, preference optimization).
Publications at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, or similar).
Familiarity with chip design or EDA environments.

This position is open to all candidates.

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8876935

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29/10/2026

Machine Learning Engineer - Agentic AI

חברה חסויה

Location: Herzliya

Job Type: Full Time

As a member of our ML group, you will design, prototype, and ship agentic GenAI systems used by chip design engineers to accelerate their day-to-day workflows. We are looking for a machine learning engineer who is energized by taking LLMs from "promising demo" to "depended-on tool": designing the agent loops, retrieval pipelines, and evaluation frameworks that make this transition real. Success in this role means agents that meaningfully shift how our customer teams work, backed by measurements you trust.

You will join a growing team of ML and software engineers building GenAI solutions for chip design teams. In this role, you will:

Design and build agentic systems featuring multi-agent coordination, long-horizon reasoning, tool use, and planning frameworks.
Develop end-to-end solutions, including domain-specific agents and high-performance retrieval pipelines spanning diverse data sources.
Partner with subject-matter experts on the customer teams to translate their workflows and rules of thumb into concrete agent assignments, prompts, tools, and guidelines.
Build the evaluation backbone for our agents: define success measures, curate offline golden sets, and instrument online telemetry to power feedback loops that drive fast, safe iteration with real cost and quality control.
Drive prompt engineering, model selection, and lightweight model adaptation as the workloads demand.
Collaborate closely with software engineers on the team to productionize your work, and with researchers and customers to shape what we build next.
Stay current with the rapidly evolving agentic and LLM landscape and bring promising techniques into our products with good engineering judgment.

Requirements:
Minimum Qualifications
3+ years of applied ML/AI experience, including hands-on work building LLM-powered or agentic applications
Strong programming skills in Python and experience with modern ML and deep learning frameworks (e.g., PyTorch)
Hands-on experience with agentic frameworks, tool use, and data pipelines
Strong evaluation mindset: comfortable designing golden sets, metrics, and telemetry to drive iteration
Excellent communication and collaboration skills, with the ability to align with subject-matter experts and translate their domain knowledge into agent behavior
Preferred Qualifications
Hands-on experience with stateful, multi-turn agentic frameworks and complex execution flows
Experience with prompt engineering and optimization techniques
Experience with model adaptation (e.g., fine-tuning, distillation, preference optimization)
Publications at top-tier venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, or similar)
Familiarity with chip design or EDA environments.

This position is open to all candidates.

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8876920

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לפני 36 דקות

AI Research Engineer - Chip Design

חברה חסויה

Location: Herzliya and Haifa

Job Type: Full Time

As a member of our ML group, you will research and build AI systems that change how our silicon is designed. Many of these problems are still open: the right way to frame the problem isn't known yet, and the data is scarce or noisy.

We are looking for a strong researcher who enjoys working closely with design teams to turn open-ended questions into well-posed research problems, and who wants to see the results used in real projects. Success in this role means models and agents that measurably improve designed chip metrics (Power, Performance, and Area) and design turnaround. You will join a growing team of AI and software engineers working with our chip design teams.

Responsibilities
Research and develop AI methods for the physical design flow, from floorplanning and placement through routing and timing closure, combining predictive models, optimization, and generative AI.
Design and build agentic workflows that connect LLMs to EDA tools, design data, and internal infrastructure, to automate and speed up tasks such as flow setup, debug, design exploration, and analysis.
Work with design teams to turn open-ended problems into well-defined research questions with clear metrics, baselines, and success criteria.
Develop robust AI methods for limited, noisy, or imbalanced data, leveraging domain knowledge and advanced learning techniques.
Own data end to end: extract useful signal from EDA tool outputs and design databases, validate and clean it, and build datasets and benchmarks where none exist.
Run careful experiments and evaluations for both models and agents, and report clearly what works and what doesn't.
Work with software engineers and design teams to bring promising prototypes into production flows, and improve them based on feedback from real projects.
Help shape new research directions as our work grows, follow the AI and EDA literature, and bring relevant ideas to the team.

Requirements:
Minimum Qualifications
MSc with a research thesis, or PhD, in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or a related quantitative field.
Research track record in which you led both problem formulation and execution, shown through peer-reviewed publications, a substantial thesis, or equivalent work.
Strong AI fundamentals and hands-on experience building, training, and evaluating deep learning models in Python (e.g., PyTorch).
Hands-on experience with generative AI, such as building and evaluating LLM-based applications or agentic workflows (e.g., tool calling, multi-agent orchestration, retrieval).
Experience getting reliable results from imperfect real-world data (small datasets, noisy labels, missing values, distribution shift), with sound judgment about when a result can be trusted.
Ability to work independently on loosely defined problems, and to explain technical results clearly to both AI specialists and hardware engineers.

Preferred Qualifications
PhD in AI, Computer Science, Electrical Engineering, or a related field
Publications at top-tier AI or EDA venues (e.g., NeurIPS, ICML, ICLR, DAC, ICCAD, MLCAD).
Familiarity with chip design flows such as physical design, place and route, and static timing analysis, or a strong interest in learning the domain in depth
Experience adapting foundation models (e.g., fine-tuning, distillation, preference optimization), building evaluation frameworks for agents, or deploying LLM systems in production.
Experience with one or more of: graph neural networks, Bayesian optimization, active learning, reinforcement learning, probabilistic modeling
Experience integrating AI models or agents into EDA tool flows through scripting (Python/TCL) or APIs.
Experience taking research into production, with solid software engineering practices.

This position is open to all candidates.

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8892480

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לפני 30 דקות

Physics & Optical Simulation Software Engineer - Q

חברה חסויה

Location: Herzliya

Job Type: Full Time

We are looking for a Physics & Optical Simulation Software Engineer to build and scale our simulation platforms and data pipelines that power our physical modeling and sensor research. While the domain involves physics and optics, this is a software-first role: you will architect modular, high-throughput simulation systems that turn theoretical formulations into production-grade software tools. We value strong software engineering discipline, expertise in Python, and the intellectual agility to quickly master new scientific domains and innovate.

Responsibilities
Architect, build, and maintain robust, end-to-end simulation pipelines and scalable developer platforms.
Design modular, high-throughput simulation workflows and infrastructure in Python and modern scientific stacks.
Partner with physicists and optical engineers to translate physical models, wave-propagation algorithms, and numerical solvers into performant, clean software modules.
Optimize simulation frameworks for performance, scalability, data throughput, and maintainability.
Develop verification suites, benchmarking frameworks, and automated regression testing pipelines to validate simulation outputs against ground-truth and physical measurements.
Establish best practices for simulation software: modular packaging, version control, CI/CD, and technical documentation within a research-driven engineering environment.

Requirements:
Minimum Qualifications
M.Sc. in Computer Science, Software Engineering, Applied Mathematics, Electrical Engineering, Physics, or a related technical field.
3+ years of professional software development experience focused on building large-scale simulations, scientific computing pipelines, or data-intensive software platforms.
Advanced proficiency in Python, including object-oriented design, performance profiling, and modern package development.
Demonstrated track record of independently learning and mastering unfamiliar technical or scientific domains to deliver innovative solutions.
Strong problem-solving skills and experience architecting large-scale, automated software workflows.
Strong cross-functional collaboration and communication skills in English.

Preferred Qualifications
Domain background or foundational understanding of wave optics, ray tracing, electromagnetics, or physical modeling.
Experience with scientific computing libraries and numerical frameworks (e.g., NumPy, SciPy, Numba, CuPy, JAX, or PyTorch).
Experience designing distributed computing workflows, batch simulation schedulers, or cluster/cloud-based execution engines.
Familiarity with C/C++ integration (e.g., pybind11, Cython) or accelerating simulation kernels via GPU/multithreading.
Experience interfacing with lab measurement infrastructure and validating simulation models against physical laboratory prototypes.

This position is open to all candidates.

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8892486

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לפני 40 דקות

ML Deployment Engineer, Edge AI - Q

חברה חסויה

Location: Herzliya

Job Type: Full Time

We are seeking a highly skilled and motivated ML Deployment Engineer to join our on-device AI team. In this role, you will take state-of-the-art machine learning models and make them run efficiently on Apple silicon - compressing, compiling, and optimizing them to deliver intelligent experiences that run entirely on-device, with the performance, power, and privacy our users expect.

Responsibilities
Own the model deployment pipeline from trained checkpoint to on-device execution, ensuring models meet product-quality bars for accuracy, latency, memory, and power.
Apply model compression techniques - quantization (post-training and calibration-based), palettization, pruning, and mixed-precision - and validate the accuracy/size/latency trade-offs of each.
Convert and compile models to the Core ML stack and target the Apple Neural Engine, GPU, and CPU.
Analyze and maximize accelerator (ANE) residency; diagnose why operations are rejected or dispatched to fallback compute units, and work with the graph to resolve them.
Profile end-to-end inference on real hardware, identify bottlenecks, and optimize compute-unit placement, memory footprint, and energy consumption.
Collaborate closely with ML research and applied ML to co-design models that are efficient to deploy and to feed hardware constraints back into model architecture decisions.
Build and maintain tooling and automation for reproducible compression, compilation, benchmarking, and numerical validation.
Stay current with the latest advances in efficient ML inference, model compression, and accelerator architectures.

Requirements:
Minimum Qualifications
Bachelors or Masters degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field, or equivalent practical experience.
5+ years of professional experience deploying machine learning models to production, with a significant focus on on-device, edge, or otherwise resource-constrained environments.
Strong programming skills in Python and C/C++.
Solid understanding of deep learning fundamentals and hands-on experience with a modern ML framework (e.g., PyTorch).
Practical experience with model compression and optimization - quantization, pruning, palettization, or mixed-precision - and a working understanding of floating-point and fixed-point/integer numerics and the quality trade-offs involved.
Experience taking models through a conversion and compilation toolchain to a hardware target (e.g., Core ML, TensorRT, TFLite, ONNX Runtime, or similar).
Demonstrated ability to profile and optimize code and models against strict latency, memory, and power constraints.
Strong debugging skills and comfort working at the boundary between ML models, compilers, and hardware.

Preferred Qualifications
Hands-on experience deploying to hardware accelerators / NPUs, and familiarity with the Apple Neural Engine (ANE), GPU, and CPU compute paths.
Experience with the Core ML toolchain and with inspecting or transforming model IRs (i.e MLIR) and writing custom graph passes.
Familiarity with reading and reasoning about compiler analytics - diagnosing operator fallback, layout, and residency issues on an accelerator.
Experience with calibration or data-based compression methods (e.g., GPTQ, activation quantization, sparsity) and layer-wise sensitivity analysis.
Background in on-device LLMs or generative models, including tokenization, KV-cache, and quantized inference.
Experience building benchmarking, numerical-validation, and CI tooling for model deployment.
Familiarity with low-level compute kernels, SoC memory hierarchies, and hardware-software co-design.
Excellent problem-solving skills, strong communication and teamwork, and a passion for building innovative products.

This position is open to all candidates.

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8892475

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תודה על שיתוף הפעולה

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01/10/2026

Researcher

חברה חסויה

Location: Herzliya

Job Type: Full Time

Our unique research team is expanding, we are looking for smart and humble team members to join the team and solve complex problems. Our technical challenges include developing new trading strategies, , improving the performance of our algorithms, finding new ways to beat the markets. As an ML researcher you will collaborate with our R&D team to execute live trading strategies. If you are looking to make an impact by putting into practice successful ideas and seeing immediately the outcome, make influence on our financial performance this is a great opportunity.

In this role you will:
Mange and lead independent research applying ML/DL methods to a wide variety of datasets.
Creatively find new trading strategies.
Work with other team members to optimize our Algorithms.

Requirements:
Masters or PhD in Computer Science, Physics, EE, Mathematics, Statistics, or a related field.
5+ Years of experience of ML research/Deep Learning etc.
Experience with software engineering in Python (must) , C++/Rust is a plus.
Strong statistical analysis and mathematical skill.
A great team player eager to share/learn/teach other team members.
Creative, self-motivated, love complex problems, determined.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8838999

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מייל

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שליחה

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המשרה נמחקה

תוכל לצפות בה בדף המשרות שלי

המשרה הוחזרה לרשימת תוצאות החיפוש

האם תרצה להסיר את המשרה מרשימת

המשרות השמורות שלך?

כן לא

אירעה שגיאה בשליחת פרטיך למשרה

לפני 23 דקות

Q - Physicist - Laser Biophysics & Safety

חברה חסויה

Location: Herzliya

Job Type: Full Time

Define experimental and theoretical research strategies to characterize light-tissue interactions, optical penetration, and thermal/mechanical effects in biological media.
Design, build, and operate experimental lab setups to investigate photo-induced tissue damage (e.g., dermal and ocular tissue models), defining precise biological damage thresholds and exposure margins.
Serve as the team's foremost authority on laser safety, leading formal laser classification (e.g., IEC 60825, ANSI Z136, FDA CDRH) and product safety compliance.
Translate complex biological damage models and physical limits into actionable system-level specifications, hardware constraints, and operational safeguards.
Define and design system level solutions for safety compliance.
Develop and validate analytical and numerical simulation models (e.g., Monte Carlo light transport, thermal bio-heat equations) to predict tissue response and optical safety margins.
Analyze and report empirical findings clearly, enabling cross-functional engineering teams to make rigorous, risk-informed architecture decisions.
Partner with multidisciplinary teams across hardware, firmware, optics, and human factors to drive feasibility from concept to safe, viable product implementations.

Responsibilities
Lead the biological and physical risk assessment for novel optical concepts, pinpointing exposure limits, hazards, and mitigation pathways.
Conduct hands-on lab experiments combining lasers and biological tissue models to measure, observe, and quantify damage mechanisms under varied irradiation profiles.
Formulate and own the formal laser product classification strategy, defining engineering controls, interlocks, and compliance documentation.
Characterize system-level physical limits and optical delivery parameters (wavelength, pulse duration, fluence, beam profile) to maximize performance while strictly adhering to safety standards.
Author comprehensive safety reports, compliance dossiers, and technical whitepapers to support engineering choices and regulatory alignment.

Requirements:
Minimum Qualifications
Ph.D in Physics, Biomedical Engineering, Electrical Engineering, or a closely related discipline.
10+ years of hands-on experience in laser physics, biomedical optics, or laser-matter interaction.
Proven track record in experimental research investigating light-tissue interaction and photo-induced tissue damage (thermal, photochemical, or photomechanical mechanisms).
Direct laboratory experience working with lasers and biological tissues (tissue optics, ex-vivo/in-vitro models, or tissue phantoms).
In-depth expertise in laser safety standards and formal classification
Strong hands-on laboratory capabilities: setting up optical beam delivery, beam profiling, dosimetry, and sensitive detection/characterization instrumentation.

Preferred Qualifications
Post-doctoral research or leadership role in biophotonics, laser-tissue dynamics, or medical laser technology.
Hands-on experience navigating regulatory approval or safety certification for consumer or medical optical systems.
Deep understanding of ultrafast / short-pulse laser interactions with biological tissue and nonlinear optical damage regimes.
Proficiency with numerical modeling of optical propagation in scattering media (e.g., Monte Carlo simulations of light transport, FEM bio-heat transfer).
Outstanding communicator with the ability to bridge fundamental biophysics, laser safety standards, and practical hardware design for cross-functional teams.

This position is open to all candidates.

עדכון קורות החיים לפני שליחה

8892495

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