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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 6 hours ago
and Industrial Hygiene. Will provide expert EHS advice--including laboratory and animal research risk assessment--that supports achievement of MIT's academic and research objectives with due
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-COPD comprises a multidisciplinary team of clinicians, experimentalists, computer scientists and patient representatives working in partnership with a medium-sized company to implement a Personalized
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 10 days ago
, and imaging techniques; and assist in lipid synthesis and experimental studies in mice/rats aimed at understanding the delivery of nucleic acids. Primary duties include 3-4 steps synthesis of novel
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as well. The salary of a trainee is €700 per month for a part-time (50%) trainee. About the position COSLAB is a tool designed to aid social scientists working with image recognition systems and label
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a part-time (50%) trainee. About the position COSLAB is a tool designed to aid social scientists working with image recognition systems and label images with them. During this internship you will
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Zukunft. Gemeinsam. Entwickeln. Die Gruppe Computer Vision & Graphics der Abteilung Vision & Imaging Technologies (VIT) ist auf der Suche nach studentischen Hilfskräften für die Entwicklung von KI
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domains such as text, code, or image generation (e.g., with Large Language Models like GPT or Llama) - Good programming skills in relevant programming languages, as well as analytical and conceptual
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, machine learning-assisted mapping and reconstruction of brain-wide circuitry, behavioral clustering, cell-type and action-specific Cal-light tagging, closed-loop optogenetic manipulation, calcium imaging
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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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Making Under Deep Uncertainties (DMDU) project aims to shift the paradigm from a traditional 'predict-then-act' model, which optimizes based on a single 'best-guess' future (forecast), to a model that