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Field
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The INM research group Structure Formation is seeking for a motivated Doctoral Researcher interested in the self-assembly of nanoplatelets (NPLs) for optical applications. This PhD project is part
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FieldChemistryYears of Research ExperienceNone Research FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria Required skills Education: Engineering degree/PhD in chemistry, materials
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technologies including thermal energy storage (TES) and hydrogen technologies integrating with concentrating solar power (CSP). Responsibilities under this position include leading research work on modeling
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engineering, data science, or related fields Strong programming skills (especially in Python), and experience with simulation, modelling, data analysis, machine learning, and hardware control Solid
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for the position. The names and contact information for two referees. One of these should be the main advisor from the PhD programme. CV Transcripts and diplomas. Relevant certificates/references. List of any works
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, epigenomics and transcriptomics from these models alongside extensive human tumour collections. You will be PhD-qualified, or have submitted your PhD thesis for examination at the point of commencing this post
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recruitment portal. Further information about the position can be obtained from Tommi Jauhiainen (tommi.jauhiainen(at)helsinki.fi ). Additional information about the recruitment process can be obtained from HR
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participation in research projects. We place great emphasis on your scientific work from the last five years. General information NTNU believes that inclusion and diversity is a strength. We want our faculty and
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partners. The PhD student to be jointly supervised by two supervisors; Prof. Hamid Reza Godini and Prof. Riikka Puurunen , will contribute to a high-impact collaborative research environment. This position
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to produce resilient and high-performing models. · PhD in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field Strong track record of applying ML in academic or industry