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characterization. Proven experience in development, operation and optimization of THz sources and detection. Experience in THz Imaging. Proficiency in Python programming to develop codes for post-data processing
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. Currently, DIMAG consists of researchers from various countries and the work is done in English. DIMAG is co-located with the IBS Extremal Combinatorics and Probability Group (ECOPRO). Website: https
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Harvard Office of Technology Development (OTD): Business Development Fellowship Eligibility: Harvard graduate students and postdocs Deadline Month: May Description: This year-long program provides
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 4 hours ago
*Applications will be reviewed on a rolling-basis. FDA Office and Location: Office of Translational Sciences (OTS) Office of Clinical Pharmacology (OCP) Division of Applied Regulatory Science (DARS
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key role in driving high-impact research, developing innovative objectives, and formulating proposals within cutting-edge fields, including Computer Vision, Digital Healthcare, AI Optimization, and
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knowledge of agentic AI/AI workflows, parallel computing, algorithms, and system performance analysis and optimization. Proficiency in programming languages such as C/C++ and Python. Demonstrated ability
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inference) Algorithmic development for bilevel (or multilevel) optimization Methodological developments in Bayesian statistics and/or decision analysis Application of adversarial risk analysis within security
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Inverse Methods and Ionospheric Modelling Research Fellow - School of Engineering - 106995 - Grade 7
to £46,049 with potential progression once in post to £48,822 Grade: 7 Full Time, Fixed Term contract up to 3 years Closing date: 15th July 2026 UK and International Travel may be required for this role
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society. For more details, please view https://www.ntu.edu.sg/ase . We are seeking a Research Fellow to join the research team led by Nanyang Assistant Professor Feng Tao in computational ecology. The role
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of efficient AI-for-Science systems, leveraging foundation models (including small language models) and parameter-efficient adaptation techniques such as LoRA, grounded in a strong understanding of optimization