Nanyang Technological University, School of Physical & Mathematical Sciences,, Division of Mathematical Sciences
Position Description
Position Title: Postdoctoral Research Fellow in
Statistics and Applied Mathematics
Position Type: Postdoc
Location: NTU, Singapore
Deadline: The position is open until filled
Research positions in Statistical Learning
at the School of Physical and Mathematical Sciences of NTU Singapore
There
will be multiple postdoc positions available in 2025 as part of the research
group of Dr. Hemant Tyagi at NTU Singapore (in the School of Physical and
Mathematical Sciences).
The
positions pertain to the research topic "Learning high-dimensional
(networked) dynamical systems" (broadly defined) and there is ample room
for exploring sub-topics depending on the interest of the candidate. The focus
will be on deriving efficient algorithms with provable statistical guarantees,
using tools from: high-dimensional statistics, optimization, probability
theory, approximation theory etc. The positions will be suitable for candidates
with an interest in high-dimensional statistics, probability and optimization.
Requirements:
- PhD in Mathematics/Computer Science/Statistics or related fields
- Good research background in Statistical learning problems -- especially from a theoretical perspective -- as evidenced by publications
- Knowledge of at least one of: MATLAB, R, Python or similar languages
The earliest starting date possible will be around June/July 2025 (but this is flexible). In general, administrative formalities can take up to 4-5 months (in the worst case) from the time an offer is made, so the exact starting date will depend on this.
Interested candidates who "fit" the criteria
should contact Dr. Hemant Tyagi by email along with their CV. Shortlisted
candidates will be contacted for an online interview.
Job Contact Name: Assoc. Prof. Hemant Tyagi
Job Contact Email: hemant.tyagi@ntu.edu.sg
Position Title: Postdoctoral Research Fellow in Mathematics
Position Type: Postdoctoral
Location: NTU, Singapore
Deadline: The position is open until filled
Research Opportunities at the Division of Mathematical Science of NTU Singapore
We are excited to announce
multiple research opportunities in mathematics at the level of PhD positions
for graduate students and postdoctoral research fellowships within the
framework of a 5-year research grant on Geometric Optimization and Design in
Learning and Robotics. The positions are ideal for pure and applied
mathematicians, physicists, and candidates with expertise in closely related
fields who are interested in research at the interface between geometry,
analysis, computation, and machine learning.
Postdoctoral positions
Applicants
for postdoctoral positions are expected to have a strong background in
mathematics with a PhD degree in mathematics or a closely related field,
combined with recent research experience in the development of mathematical
results or tools with potential or realised applications to problems in fields
such as data science, robotics, and machine learning. Postdoctoral appointments
will be for an initial duration of 1 year with the possibility of renewal to 2
or 3 years depending on research outcomes. Postdoctoral appointments can
commence at any time during the year.
Requirements:
- PhD
degree in Mathematics/Physics/Computer Science/Statistics or related
fields
- Evidence
of excellence in at least one of the following areas: analysis,
differential geometry, optimization, probability and statistics
- Evidence
of recent high-quality research output
- Good communication skills including fluency in English
Interested candidates should contact Asst. Prof. Cyrus Mostajeran with their CV for consideration.
Job Contact Name: Cyrus Mostajeran
Job Contact Email: cyrussam.mostajeran@ntu.edu.sg
Position Title: Postdoctoral Research Fellow in
Computational and Applied Mathematics
Position Type: Postdoctoral
Location: NTU, Singapore
Deadline: The position is open until filled
Research Opportunities at the School of Physical and
Mathematical Sciences of NTU Singapore
We
are excited to announce multiple research opportunities in applied mathematics
at the level of PhD positions for graduate students and postdoctoral research
fellowships within the framework of a 5-year research grant on Geometric
Optimization and Design in Learning and Robotics. The vacancies are targeted at
applied mathematicians, computational scientists, physicists, and candidates
with expertise in closely related fields who are interested in research at the
interface between geometry, mechanics, materials science, computation, and
control theory.
Postdoctoral position
Applicants for postdoctoral positions are expected to have a strong background in applied and computational mathematics with a PhD degree in mathematics, physics, mechanical engineering, or a closely related field, combined with recent research experience in the development of mathematical or computational tools with applications to problems in mathematical biology, mechanics, control theory, or robotics. Postdoctoral appointments will be for an initial duration of 1 year with the possibility of renewal to 2 or 3 years depending on research outcomes. Postdoctoral appointments can commence at any time during the year.
Requirements:
- PhD
degree in Mathematics/Physics/Computer Science/Mechanical Engineering or
related fields
- Evidence
of excellence in at least one of the following areas: analysis,
differential geometry, optimization, mechanics, scientific computing,
control theory
- Evidence
of recent high-quality research output
- Good communication skills including fluency in English
Interested candidates should contact Asst. Prof. Cyrus
Mostajeran with their CV for consideration.
Job Contact Name: Cyrus Mostajeran
Job Contact Email: cyrussam.mostajeran@ntu.edu.sg
We are excited to announce an opportunity at School of Physical and Mathematical Sciences (SPMS) in Nanyang Technological University, Singapore. The Complex Systems Science @ NTU (led by A/Prof Cheong Kang Hao) and the STEM Learning Optimisation CoLab (led by Dr. Darren Yeo) are looking for passionate individuals to join our inter-disciplinary Science of Learning research team as a Research Fellow.
The successful candidate will be an integral member of an inter-disciplinary Science of Learning research team developing brain-based machine-learning predictive models for early identification of mathematical learning difficulties in kindergarten and early primary level students.
Key Responsibilities:
Conduct independent research
Handle grant-related administrative tasks (e.g., prepare and amend ethics documents)
Conduct literature reviews, and prepare research progress reports and policy reports for stakeholders
Supervise and lead a team of full-time and part-time research staff
Play a central role in coordinating and running of neuroimaging study
Develop and/or manage data analysis pipelines
Coordinate between and liaising with faculty, collaborators, and stakeholders from different disciplines
Carry any other duties as required by PI and co-PI(s)
Job Requirements:
PhD degree in experimental psychology, biopsychology, cognitive psychology, educational neuroscience, NeuroAI, neuropsychology, neurology, psychiatry, cognitive neuroscience, AI in education, deep learning, biomedical engineering, computer science, and related fields
Excellent oral and written communication skills with good command of the English language to perform literature reviews and write progress and research reports
Knowledge and experience in social science research methodologies
Good statistical knowledge and skills, including familiarity with statistical analysis tools (e.g., SPSS and R)
Familiarity with neuroimaging software (e.g., SPM, AFNI, FSL)
Familiar with coding (e.g., in R, MATLAB, Python)
Experience with research project management and supervision of research staff and students
Excellent organizational skills, project coordination and administration skills (e.g., in data management and storage), and able to work within tight deadlines
Good leadership skills
Self-directed learner who can effectively picks up relevant skills as needed
Self-motivated, able to work independently to collect data, analyse data, and communicate findings
Experience with conducting cognitive neuroscience and/or neuroimaging research with children and/or adults, especially using fMRI, will be preferred
Experience with resting-state fMRI and/or naturalistic fMRI analyses will be preferred
Experience with cognitive/neuropsychological assessment with children and/or adults will be preferred
Experience with dissemination of research findings (e.g., conference presentations and research reports) and academic publication processes will be preferred
Good knowledge of cognitive functions and cognitive neuroscience theories and techniques
Good or working knowledge of machine learning techniques
We regret to inform that only shortlisted candidates will be notified.
A Postdoctoral Research Fellow position is available in the School of Physical and Mathematical Sciences at Nanyang Technological University with a focus on robust optimization and procurement management. The post-holder will be encouraged to pursue his or her own, and newly arising, applications using a data-driven robust optimization approach.
The College of Science seeks a diverse and inclusive workforce and is committed to equality of opportunity. We welcome applications from all and recruit on the basis of merit, regardless of age, race, gender, religion, marital status and family responsibilities, or disability.
Key Responsibilties:
Conducting research on online resource allocation with limited offline data for generalized assignment problems.
Developing collaboration with other research teams, public agents and particularly industry partners.
Providing basic administrative support.
Job Requirements:
Ph.D. in Operations Research, Mathematical Optimization, Industrial Engineering, or a related field.
Expertise in at least one of the areas of convex optimization, robust optimization and supply chain management.
Strong coding skills (one or more of R, Julia or Python) and experience using one or more commercial optimization solvers.
We regret to inform that only shortlisted candidates will be notified.
- publication record in the relevant areas
- strong R/MATLAB programming skills
- strong ability to work independently and as part of a team with strong initiatives.
- stochastic optimal control
- model uncertainty in finance
- distributionally robust optimization (DRO)
- financial and insurance mathematics
- machine learning algorithms, their convergence rates and complexity analysis, and their applications in finance and insurance
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