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criteria The candidate should hold a PhD in plasma physics and chemistry, materials science, surface science, polymer chemistry, or a closely related scientific field. Experience in low-pressure plasma
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. Under the supervision of Luke Johnson, PhD and Jerrold Vitek, MD PhD, the associate will work collaboratively within the NMRC on an existing project supporting the collection and analysis of large-scale
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GESIS - Leibniz Institut für Sozialwissenschaften | Mannheim, Baden W rttemberg | Germany | 7 days ago
collection Presenting and publishing ESS-relevant research in scientific conferences and journals Your profile: Above-average completed PhD (as well as a Master) in the Social Sciences (for example Sociology
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have a PhD, or be close to completion, in psychology, neuroscience, developmental science, medicine or a related discipline, and ideally have experience of standardised neurodevelopmental or behavioural
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presentations, and lab collaborations; mentorship of lab graduate and undergraduate students Qualifications Requirements: A PhD in Computer Science, Information Science, or a related field Must have a strong
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. Qualifications Essential Qualifications: PhD in computer science/engineering with expertise in a security or privacy research topic. Less than 5 years post-receiving the Doctoral degree. Excellent analytic, design
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About us The researcher will join the Human-Centred Computing (HCC) group at King’s at the Department of Informatics, which provides a supportive and collaborative environment and an exciting mix
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Eligibility criteria Selection will be based on the following scientific and technical criteria: • PhD in computational biology, machine learning, bioinformatics or a related field. • Proficiency with Python
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Information Eligibility criteria The applicant should have a PhD degree (or equivalent) in Computer Science or Mathematics and good knowledge in symmetric key cryptology. Good programming skills are required
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-0157/25], carried out under the First Team programme of the Foundation for Polish Science, co-financed by the European Union under the European Funds for Smart Economy 2021–2027 (FENG). Quantitative