153 parallel-computing-numerical-methods-"Prof" research jobs at Hong Kong Polytechnic University
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experience at the time of application; and (b) be familiar with theory of condensed matter, DFT, EPW, tight binding methods, ab initio modeling of electron-phonon mediated superconductivity and the
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project - “Research on methods for distributed workshop scheduling considering loading efficiency”. Qualifications Applicants should: (a) have an honours degree or an equivalent qualification; (b
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research experience in at least one of the following areas: (i) nonlinear response effects; (ii) ferroelectric materials; (iii) first-principles method development; and (iv) domain wall physics. Applicants
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Department of Computing Part-time Research Assistant (one to two post(s) posts) (Ref. 250808017) [Appointment period: each for four months initially, with possibility of further appointment] Duties
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projects on-time with minimum supervision. Preference will be given to those with research experience in machines designs and development of computer programmes for numerical computation of electromagnetic
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given to those with research experience in drone delivery optimization, machine learning, reinforcement learning and operation research. Applicants are invited to contact Prof. Siyuan Feng at telephone
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technical writing and experience in assisting research publication. Preference will be given to those who excel in impact-resistance measurement and computational software such as ABAQUS and PFC3D. Applicants
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- “Development of high-performance two-dimensional material based ultrafast photonic and optoelectronic devices using aerosol jet printing method”. Qualifications Applicants for the Research Fellow post should
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experience. Applicants are invited to contact Prof. Chung Sai-ho at telephone number 3400 3790 or via email at nick.sh.chung@polyu.edu.hk for further information. Conditions of Service For full-time
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, Operations Research, Computer Science or a related discipline and must have no more than five years of post-qualification experience at the time of application; and (b) experiences in optimization modeling