2016
Great Minds student internships

Pitch your vision of the most exciting IT challenges
and win an internship at IBM Research

Topics at the Zurich Lab

Z-2016-01 Storage security & cloud
Z-2016-02 OS kernel security
Z-2016-03 Cloud analytics acceleration
Z-2016-04 Storage techniques for Big Data
Z-2016-05 Accelerator technologies
Z-2016-06 Solid-state storage
Z-2016-07 Predictive caching
Z-2016-08 Machine learning for IT services
Z-2016-09 Predictive analytics: From correlations to causality
Z-2016-10 Performance optimization of predictive analytics tool suite
Z-2016-11 Implementation of 'Minority Report'-like interaction tool with knowledge graphs
Z-2016-12 Vision-based 3D scene representation & segmentation
Z-2016-13 Computational methods for cancer personalized medicine
Z-2016-14 Accelerated deep neural networks for cognitive systems
Z-2016-15 Fast, approximate computation of trigonometric functions with SIMD instructions on modern architectures
Z-2016-16 Quantum simulations of large scale biological systems
Z-2016-17 Digital pathology
Z-2016-18 Technical diagram understanding
Z-2016-19 Efficient sparse matrix-matrix multiplication on IBM POWER9/NVIDIA Volta-based hardware

Topics at the Ireland Lab

D-2016-01 Characterizing relationships from text
D-2016-02 Decision gisting
D-2016-03 Populating & reasoning with a knowledge graph of risk factors in the domain of breast cancer
D-2016-04 Robust activity planning and scheduling with multi-modal travel
D-2016-05 Social networks of sellers inside IBM
D-2016-06 Distinguishing between incomparability and indifference in preferences
D-2016-07 Exploring sentiment composition
D-2016-08 Multi-robot collaboration
D-2016-09 Personalized health
D-2016-10 Machine learning at scale with Spark
D-2016-11 Cognitive IoT smart city sensing
D-2016-12 Multidimensional professional graph analytics
D-2016-13 Mobility patterns estimation in indoor and outdoor environment
D-2016-14 Network level description of traffic in large urban areas
D-2016-15 Hydraulic diffusivity inversion for a confined inhomogeneous aquifer
D-2016-16 Design of adaptive decentralised train control algorithms for future rails
D-2016-17 Hybrid methods for polynomial optimisation
D-2016-18 Novel models of power systems dynamics
D-2016-19 Core machine learning on Spark
D-2016-20 Distributed data science for IoT
D-2016-21 Cognitive environmental data analytics
D-2016-22 Self optimizing distributed platform for data-science
D-2016-23 Anomaly detection and diagnostic for time series on networks
D-2016-24 Modelling demand response in energy forecasting services
D-2016-25 Applying network inference to estimate missing links in a power supply network
D-2016-26 Development of integrated surface water - groundwater analytic element models
D-2016-27 Watson health
D-2016-28 In-memory Big Data analytics foundations
D-2016-29 Towards bespoke force-fields for industrially relevant chemical systems
D-2016-30 Let me out: Taking memory out of the server box
D-2016-31 Electric vehicle integration in the smart grid
D-2016-32 Optimising the location of connected smart devices