Mastereksamen

Kommende

Tid og sted: 16. des. 2019 09:15, Lille fysiske auditorium, Fysikkbygningen

"Latent Variable Machine Learning Algorithms: Applications in a Nuclear Physics Experiment"

Tid og sted: 17. des. 2019 09:30, Rom Ø397, Fysikkbygningen

"Studies of Quantum Dots using Machine Learning"

Tid og sted: 17. des. 2019 10:15, Kristian Birkelands auditorium, Fysikkbygningen

"Immunogenic calreticulin signaling in lung and glioblastoma cancer cells after x-ray and proton irradiation - Protocol development and optimization"

Tid og sted: 17. des. 2019 13:45, Kristian Birkelands auditorium, Fysikkbygningen

"Classical Molecular Dynamics using Neural Network Representations of Potential Energy Surfaces"

Tidligere

Tid og sted: 28. nov. 2019 11:00, Lille fysiske auditorium, Fysikkbygningen

"A Practical Approach to Compare Time Domain and Frequency Domain Bioimpedance Measurements"

Tid og sted: 18. nov. 2019 12:30, Rom Ø397, Fysikkbygningen

"MRI based detection of time-of-day variation in white matter microstructure - are more complex diffusion models better?"

Tid og sted: 16. okt. 2019 14:00, Rom Ø397, Fysikkbygningen

"Industrial Multi-step Time Series Forecasting with Machine Learning Methods"

Tid og sted: 11. okt. 2019 10:30, Lille fysiske auditorium, Fysikkbygningen

"Coulomb excitation of neutron-deficient 140Sm"

Tid og sted: 27. sep. 2019 11:15, Lille fysiske auditorium, Fysikkbygningen

"Decameter scale irregularities in the polar ionosphere"

Tid og sted: 26. sep. 2019 09:15, Styrerommet, Ole Johan Dahls hus rom 4118

Offset Correction for Swept Threshold Ultra-Wide-Band Sampling

Tid og sted: 24. sep. 2019 10:00, Rom Ø397, Fysikkbygningen

Real-time quantum many-body dynamics

Tid og sted: 23. sep. 2019 13:45, Rom Ø397, Fysikkbygningen

AQUADUCT Ab Initio Quantum Dynamics Using Coupled Cluster in Time

Tid og sted: 23. sep. 2019 10:00, Rom Ø397, Fysikkbygningen

Learning Correlations in Quantum Mechanics with Neural Networks

Tid og sted: 18. sep. 2019 09:30, Rom V207, Fysikkbygningen

"Naturforbruk og bærekraft. En diskursanalyse av NOU 2005: 5"

Tid og sted: 17. sep. 2019 10:15, Rom Ø397, Fysikkbygningen

Embedded Development of a Wireless SoC Instrument for Electrical Impedance Spectroscopy on Cells - A Prototype Platform for Medical Applications

Tid og sted: 9. sep. 2019 13:00, Rom 301, Gunnar Randers vei 19

Efficient Annotation of Semantic Segmentation Datasets for Scene Understanding with Application to Autonomous Driving

Tid og sted: 4. sep. 2019 09:30, Rom 408, Gunnar Randers vei 19

Adaptive Power Control in Peer to Peer Networks

Tid og sted: 30. aug. 2019 09:30, Rom V139, Fysikkbygningen

"Bioimpedance on ASIC: Design of a 350 nm CMOS Analog Front-End of a 4-Electrodes-Compatible Measurement System for Impedance-Based Monitoring of Cell Cultures"

Tid og sted: 19. aug. 2019 14:15, Rom Ø394, Fysikkbygningen

Simulations and means of characterization of gravity-stabilized flow on a self-affine surface

Tid og sted: 16. aug. 2019 10:00, Rom Ø394, Fysikkbygningen

Atomistic Modelling of Creep and Flow in Silica-Water Systems

Tid og sted: 16. aug. 2019 09:15, Kristian Birkelands auditorium Fysikkbygningen

Evaluation of image quality for metal artifact reduction reconstruction techniques using a novel quality phantom especially designed for metal artefact evaluation

Tid og sted: 14. aug. 2019 10:00, Kristian Birkelands auditoriumFysikkbygningen

Deep-level transient spectroscopy system with a response time in the microsecond time frame

Tid og sted: 6. aug. 2019 10:00, Lille fysiske auditorium, Fysikkbygningen

Solving the mysteries of 133Xe with inverse kinematics.

Nuclear level density and Ƴ-ray strength function for 133Xe using the inverse-Oslo Method.

Tid og sted: 4. juli 2019 11:00, Kristian Birkelands auditorium, Fysikkbygningen

Solving SU(3) Yang-Mills theory on the lattice: a calculation of selected gauge observables with gradient flow

Tid og sted: 2. juli 2019 10:30, Rom 301, Gunnar Randers vei 19

Acoustic Recognition with Deep Learning; Experimenting with Data Augmentation and Neural Networks