207 computer-programmer-"https:"-"CNR"-"https:"-"https:"-"https:"-"https:" Fellowship positions in Singapore
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using computational pipelines for microbiome and metabolite profiling, ensuring high scientific rigor and reproducibility. Contribute to manuscript writing, conference presentations, and preparation
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that will support regulatory approval and market access. Requirements • Doctoral degree in computer science or statistics. • Commensurate experience (2 years) in clinical research. • Experience with
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Plan and perform the collection and organization of corporate financial and sustainability disclosures Plan and perform the collection and organization of information regarding government policies and
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialization and deployment. ERI@N has multiple Interdisciplinary Research Programmes which
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expected, and familiarity with modern machine learning methods will be considered an asset. NUS offers a vibrant research environment, with access to high-performance computing facilities and opportunities
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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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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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culture assays to assess cell–material interactions. Integrate experimental and computational workflows; analyse, document, and report results. Prepare scientific manuscripts and grant proposals
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, or related fields majoring in Computational Fluids Dynamics (CFD); • Well informed and knowledgeable in urban climate modelling; • Highly experienced in using environmental simulation tools, such as
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discipline (statistics, mathematics, computational biology, data science). Strong programming skills (R preferred) Statistical competence (Bayesian is advantageous) Please email the PI at ephdbsl