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identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision
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oriented field relevant to the project, such as computer science, mathematics, statistics or physical sciences. Expertise in machine learning based and statistical data analysis, including the capability
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-Phenomenology (hep-ph) , HEP-Theory (hep-th) , High Energy Physics , High Energy Theory , Machine Learning , Particle Physics , String Theory/Quantum Gravity/Field Theory , string-math Appl Deadline: 2026/03/31
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. The candidate should have a strong background in process modeling, control, optimization, applied machine learning, and AI. City: Cranbury State: NJ Location: Off Campus Create a Job Match for Similar Jobs About
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Postdoc Position (f/m/d) for any of the following topics: Combining non-equilibrium alchemistry with machine learning Free energy calculations for enzyme design Permeation and selectivity mechanisms in
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machine learning approaches where applicable. Provides feedback and guidance to wet-lab scientists on experimental design. Summarizes research findings and publish results in research journals. Assist with
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Geospatial analysis, machine learning, and predictive modelling, Have a good command of programming tools such as R packages, Phyton, and other programming languages Publications in the field Excellent
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, including in computational tasks where data visualization, preprocessing, or interpretation can be improved. Devises and deploys custom machine learning approaches where applicable. Provides feedback and
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reactions. We welcome applicants from diverse backgrounds, including computational chemistry, bioinformatics, systems biology, and machine learning. The project offers a unique opportunity to collaborate
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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological