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), based within the Department of War Studies at King’s College London, is seeking a Research Fellow for its new Net Assessment Programme. They will be engaged on a new multi-year research project funded by
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are highlighted below: - Cultivated meat: examples include a research program on cell line development, cell engineering, process optimisation, media optimisation, product development and downstream processing, and
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about to obtain) a PhD in Computer Science or a relevant field. A demonstrable research record in an area related to cyber security and programmable networking. A strong publication record, commensurate
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and grant applications. Key Responsibilities Provide intellectual and technical leadership in bioinformatics and computational biology to support high-impact, hypothesis-driven research. Lead and
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: Presentation of the academic qualifications and/or diplomas, if applicable. Enrolment in PhD program. Work plan: The Fellowship Holder will be a contributor to the FABLE-IKI project, through the following
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mentoring at the postdoctoral level. In addition, the candidate may join the LEAD Graduate School & Research Network to develop cross-faculty collaborations with researchers in the field of education. The
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Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual
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PhD Fellow in Anthropology: Bushmeat, Infrastructure and Zoonotic Risk in Africa–China Contact Zones
Investigator). The position is for a period of three years. The objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite for
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(R1) Positions PhD Positions Application Deadline 31 Mar 2026 - 23:59 (Europe/Lisbon) Country Portugal Type of Contract Not Applicable Job Status Not Applicable Is the job funded through the EU Research
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure