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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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development using Python and/or other programming languages, as well as executing organic (multistep) reactions in the laboratory. We are looking for an applicant with: A Master's degree in chemistry, chemical
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and machine learning is necessary. Additional knowledge in optimization, reinforcement learning, and model predictive control is highly desirable. Technical Proficiency: Strong skills in Python and
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, experience working with health data is a plus but not a requirement Proficiency in Python and experience working in Linux-based HPC environments or cloud computing platforms Proven experience with deep
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, or a related discipline Demonstrated machine learning experience, experience working with health data is a plus but not a requirement Proficiency in Python and experience working in Linux-based HPC
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of LIDAR systems, especially FMCW LIDAR architectures Programming experience in MATLAB or Python for data analysis Hands-on experience building and optimizing test setups for photonic devices We are looking
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scientific writing and communication skills very good knowledge of statistics and quantitative data analysis, hands-on experience with R or Python strong interest in prototyping commitment to and interest in
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an advantage. Technical skills Proficiency in computational design tools (e.g., Grasshopper, Rhino, Python). Familiarity with immersive environments (Unity, Unreal Engine). Experience with AI-driven
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challenges. Additional qualifications considered an asset: experience in Python/Matlab coding, cryogenic measurements, electronics design, device design, and modelling. Your responsibilities include: Improving
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to implement data analytics processes and algorithms in scientific programming languages (e.g., Python, R). The ability to collaborate within a multidisciplinary research group. Independent work, collaboration