[robotics-worldwide] [jobs] 2 Ph.D. positions in Machine Learning @Lund/Stanford

Luigi Nardi luiginardi at gmail.com
Wed Jul 10 22:12:45 PDT 2019


We seek two Ph.D. students in Machine Learning at Lund University (Sweden).

*Position type*: Ph.D. scholarship
*Research area*: Machine Learning
*Start*: September 2019 or later
*Duration*: 4 years
*Where*: Lund University - CS department
*Application closing date*: July 29, 2019
*Supervisor*: Professor Luigi Nardi (luigi.nardi at cs.lth.se)
*Position description*: https://urldefense.proofpoint.com/v2/url?u=https-3A__lu.varbi.com_en_what-3Ajob_jobID-3A280143_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=lq0jBqUO9REk5U6Pbryoi7DW_ECDWR8gJLVggDAeH5c&s=cE6HvbpV9uAzrEIGmR_vnDoz_qIJNB_wDqFzL_9xGHk&e= 

These projects, financed by WASP <https://urldefense.proofpoint.com/v2/url?u=https-3A__wasp-2Dsweden.org_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=lq0jBqUO9REk5U6Pbryoi7DW_ECDWR8gJLVggDAeH5c&s=B4HT_Zp6WNfrXBwGBqfcsGckFbuEt-stbBJ7iZGITgI&e= > (Wallenberg AI,
Autonomous System and Software Programme), aim at introducing innovative
algorithms and methodologies to overcome the limitations of multi-objective
black-box optimization. They are part of a collaboration with Stanford
University. The students will be encouraged to apply to the WASP exchange
program with Stanford to work closely with collaborators.

*Apply here <https://urldefense.proofpoint.com/v2/url?u=https-3A__lu.varbi.com_en_what-3Ajob_jobID-3A280143_&d=DwIBaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=lq0jBqUO9REk5U6Pbryoi7DW_ECDWR8gJLVggDAeH5c&s=cE6HvbpV9uAzrEIGmR_vnDoz_qIJNB_wDqFzL_9xGHk&e= >.*

Topics of interest:

   - Black-box optimization
   - Derivative-free optimization (DFO)
   - Bayesian optimization
   - Algorithm configuration and selection
   - Active learning
   - Automated machine learning (AutoML)
   - Neural architecture search (NAS)
   - Hyperparameter optimization
   - Learning to learn
   - Meta learning and transfer learning
   - Reinforcement learning (RL)
   - Optimization of neural networks
   - Evolutionary algorithms (EA)
   - Discrete optimization and NP-hard problem solving
   - Data-driven analysis of algorithms, hyperparameter importance, etc.

Some applications of interest:
Image classification, Natural Language Processing (NLP), Simultaneous
localization and mapping (SLAM), Design space exploration (DSE), Optimizing
compilers, Hardware design: CPU, GPU, FPGA, CGRA, ASIC.


-- 
Luigi Nardi, Ph.D.
CS and EE Departments
Stanford University
353 Serra Mall, Stanford, CA 94305, USA


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