Research Fellow, Machine Learning
7 days left
- NATIONAL UNIVERSITY OF SINGAPORE
- 01 Mar 2023
- End of advertisement period
- 31 Mar 2023
- Academic Discipline
- Physical Sciences, Mathematics & Statistics, Physics & Astronomy
- Job Type
- Academic Posts, Research Fellowships
- Contract Type
- Fixed Term
- Full Time
Postdoctoral Research Fellow (Machine Learning)
The Department of Geography at the National University of Singapore (https://fass.nus.edu.sg/geog/) is seeking one full-time Postdoctoral Research Fellow (RF) with relevant experience to work on assessing the impact of climate extremes in . The RF is expected to integrate advanced knowledge in data science to explore patterns in the plethora of open-access observations (e.g., satellite remote sensing, eddy covariance) of the earth system, in particular focus on the extreme climate in tropical Asia.
The ideal candidate is expected to be highly creative, have strong numerical and critical thinking skills, a working familiarity with the processes governing the terrestrial carbon cycle and the ability to work in a diverse environment. This position is funded by the NUS presidential young professor award. The initial contract will be 1 year with the possibility of renewal contingent on satisfactory job performance and funding availability. We provide competitive salary (> S$65k/year) for the position with the consideration of years of experience postgraduate.
The duties of the RF include:
- Collect and organize large open-access data relevant to the terrestrial carbon cycle.
- Implement advanced machine learning method to explore patterns in big data.
- Develop algorithms to examine climate-carbon cycle interactions under extreme conditions.
- Presentation of results at international conferences.
- Writing and publishing in scientific journals.
Interested candidates should submit an application comprising: 1) a letter of interest with details of their research experience, interest and future plan (1-2 pages), 2) a full curriculum vitae, with contact information for three references (one of which must be from your PhD supervisor) and 3) the PDF of your best paper (optional). For full consideration, please submit your application by Oct 30th, 2022. The posting shall remain open until the position is filled.
NUS is one of the top universities in Asia, and its geography department has recently been ranked as the 5th best in the world. This position offers an excellent environment for working with a highly skilled interdisciplinary team and will provide chances to collaborate with researchers at both national and international institutes.
Please contact Assistant Prof. Xiangzhong (Remi) Luo (firstname.lastname@example.org) with any questions regarding the position or application process.
- PhD in data science, mathematics, physics or a related field (preferably less than 3 years postdoctoral experience).
- Proficient coding skills in at least one of Matlab, R and Python.
- Experience processing large dataset and implementing mainstream machine learning techniques.
- Basic understanding of the application of machine learning in earth system science.
- Strong numerical and communication skills.
- Evidence of ability to publish results in relevant journals.
- An ability to work in an integrated team environment.
At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presence is required.
Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS.
Location: Block AS2-03-01
Organization: National University of Singapore
Department : Geography
Employee Referral Eligible:
Job requisition ID : 17567