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Field
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postdoctoral position is available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research at the intersection of Geometry
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research background in an area of computational pure mathematics (such as algebra, arithmetic geometry, number theory, group theory, representation theory, commutative algebra, or algebraic geometry
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Nov 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Feb 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The appointed postdoc will lead the computational work
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to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and
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18 Sep 2025 Job Information Organisation/Company Università degli Studi di Milano-Bicocca Research Field Mathematics » Geometry Researcher Profile Recognised Researcher (R2) Leading Researcher (R4
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural
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. Experience with the study of elliptic curves, and in Galois theory, difference equations, and algebraic geometry. Education in computer science and applied math. Preferred Qualifications: Teaching competitive
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, ranging from AI to novel geometry modelling techniques. While this position is initially for a fixed term period, depending on the success of the role, opportunities will be explored to extend it beyond
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for emulating ice-shelf melt rates for a range of ocean thermohaline conditions and ice-shelf cavity geometries produced by the MITgcm model in a coupled configuration with a dynamical ice-flow model