The IBM Research Intern Program

A unique opportunity to work alongside world-class scientists

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Z-2019-1

Project 1: Simulation of a nanofluidic neuromorphic device

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Department:   Science & Technology
Mentor/Manager:   Dr. Rolf Allenspach

Brief description:  
Analog memory hardware devices for brain-like computing promise to deliver huge improvements in power efficiency compared to today’s GPUs. However, current implementations do not respond symmetrically with sufficient resolution to training inputs. We want to explore nanofluidic devices based on gold nanoparticles in water because they can be made symmetric by design. Existing simulations of Brownian motion in static 2D energy landscapes will be complemented with the simulation of electro-osmotic flows in these devices. Knowledge of physics and C++/Python is required.

Z-2019-2

Project 2: VO2 switches for neuromorphic devices

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Department:   Science & Technology
Mentor/Manager:   Dr. Siegfried Karg

Brief description:  
We are looking for an outstanding summer student for our activities based on VO2 insulator–metal transition switches. The electrical resistance of VO2 changes by several orders of magnitude at the phase-transition temperature. We exploit this phenomenon to build electronic devices for neuromorphic computing applications.

We offer the opportunity to work in a state-of-the-art exploratory research facility with close interaction with leading experts in the fields of nanofabrication and nanoscale device measurements. You will join our nanoelectronics team and contribute to the design of nanostructures and their fabrication, as well as performing material and device characterization measurements. You will have the opportunity to work in a collaborative and creative group in a lively research environment.

Applicants are expected to have a physics or engineering background in any of these topics: nanometer-scale science, nanofabrication, electrical device characterization or circuit design. The ideal candidate is very talented, creative, communicative and highly motivated. This position is available this summer for a duration of 2–3 months.

Z-2019-3

Project 3: Electron devices using Weyl semi-metals

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Department:   Science & Technology
Mentor/Manager:  Dr. Bernd Gotsmann

Brief description:  
We are looking for a summer student for our activities in electron devices using Weyl semi-metals, which have been demonstrated only recently. They form a novel material class and exhibit extreme properties such as record-high magnetoresistance or macroscopic scattering lengths. We have found that some Weyl semi-metals also exhibit hydrodynamic electron flow, i.e. charge no longer flows diffusively like in ordinary metals or semiconductors but behaves like a viscous liquid. The technological prospects of these characteristics are yet to be explored, and this project is part of a pioneering effort to do so.

We offer the opportunity to work in a state-of-the-art exploratory research facility with close interaction with leading experts in the fields of nanofabrication, nanoscale devices and low-level transport measurements. You will join our nanoelectronics team and perform experiments and electrical measurements to develop our Weyl semi-metal material platform. You will have the opportunity to work in a collaborative and creative group in a lively research environment.

Applicants are expected to have a physics or engineering background in any of these topics: nanometer-scale science, nanofabrication, material or electrical characterization. The ideal candidate is adventurous, communicative and highly motivated. This position is available for a duration of 2–4 months.

Z-2019-4

Project 4: Demonstration of interplay between neural network software algorithms and novel hardware accelerators

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Department:   Science & Technology
Mentor/Manager:  Dr. Jonas Weiss

Brief description:  
To overcome compute bottlenecks for AI and machine-learning applications, we have developed different non-volatile, in-memory and optical technology candidates to build highly power-efficient analog compute engines. These engines accelerate algorithmic core operations like matrix vector multiplications, matrix transpose and parameter/weight updates. To assess performance at an early stage, we have also built a Python/TensorFlow simulation framework to interact directly with the physical hardware on the test bench (measurement setup). In this summer internship, the student will run and optimize MNIST concurrently on a host computer with different analog hardware engines on the test bench.

Z-2019-5

Project 5: Characterization and control of non-volatile analog memory elements

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Department:   Science & Technology
Mentor/Manager:  Dr. Stefan Abel

Brief description:  
At IBM Research – Zurich, we have established several technologies and materials for the realization of nonvolatile resistive memory elements. In this internship, measurements will be performed on these materials and devices to characterize, interpret and understand the suitability for applications in the training of deep neural networks.

Z-2019-6

Project 6: Machine learning for electronic tongues

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Department:  Science & Technology
Mentor/Manager:  Dr. Patrick Ruch

Brief description:  
Cross-sensitive sensor arrays, also called “electronic tongues”, are a promising technology to generate unique chemical fingerprints of liquids and can be applied to various domains such as food safety, quality control or healthcare. Sensor arrays based on potentiometric measurements feature very low power consumption and are therefore suitable for portable or remote applications. Machine learning is an essential component of electronic tongues and involves both supervised and unsupervised learning and classification methods, depending on the context.

In this internship project, data from exploratory electronic tongue devices will be processed by machine-learning algorithms in order to classify different types of liquids, and to perform multivariate calibration in order to correlate electronic tongue data quantitatively with concentrations of dissolved compounds.

Recommended background: Basic knowledge of machine learning and tools for implementation are recommended (e.g. Python).

Z-2019-7

Project 7: Privacy and ethics-compliant classifier for AI in healthcare

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Department:   Science & Technology
Mentor/Manager:  Dr. Thomas Brunschwiler and Dr. Rui Hu

Brief description:  
The potential of AI in healthcare is tremendous, particularly in combination with data acquired from smart sensors. By tracking patients’ symptoms continuously and objectively, we can model the disease progress of individuals even outside of the hospital setting. As a result, a patient’s quality of life can be improved through preventive care and disease management.

However, data privacy and ethical best practices need to be respected during exploration, training and maintenance of such systems. In clinical trials, it is best practice to delete raw data at the end of a study. Thus, a follow-up project cannot use data harvested from the previous project. In other cases, patients might agree to the recording of sensitive personal data only as long as it is not shared, or even transferred to the cloud. Thus, data remains distributed on patients’ personal devices.

Considering these constraints, we offer an internship position to explore methodologies such as online, hierarchical, adaptive, and federated learning to unlock the full potential of AI algorithms in the field of healthcare. The student will establish and implement classifier designs compatible with spatial or temporal partitioned training data sets. The proposed training pipeline will be demonstrated and benchmarked for audio analytics purpose, to build a cough or activity-of-daily-living classifier without the need to centralize sensitive data and to extended it with additional classes from data acquired in subsequent clinical trials.

Z-2019-8

Project 8: Enhancing a computational framework to analyze line and scatter plots

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Department:   Cognitive Computing & Industry Solutions
Mentor/Manager:  Dr. Maria Gabrani

Brief description:  
Scientific documents such as papers, reports and patents but also other professional documents such as financial or medical reports very often include numerous graphs. The purpose of these graphs is to illustrate, in a graphical way, data sets that explain, describe or emphasize the textual content of those documents. Such data sets can be generated through experiments, measurements, observations or other means, and are uniquely depicted in graphs for the reader to extract the message in a fast and efficient way. With the emergence of internet searches, archival storage and the speed at which new scientific documents are created, it would be of great value to have a tool that can automatically scan numerous documents, extract the main scientific knowledge and present it in a concise and meaningful way. However, for a document to be completely and thoroughly analyzed, its graphs also need to be processed and the main knowledge, as presented by the depicted data sets, extracted. However, as such graphs are stored primarily as bitmap images, the data sets are frequently noisy, with the graphical symbols used to depict them, such as lines, markers and text, appearing in an overlapping, overriding or intersecting manner. At IBM Research – Zurich, we are developing computational techniques based on image processing and machine learning to identify graphical symbols automatically, extract their semantics and ultimately capture the data (knowledge) they represent. From the taxonomy of various graphs, we are currently focusing on line and scatter plots, phase diagrams and forms.

For our growth in the area of extracting knowledge from scientific graphs, we are looking for motivated candidates to enhance our computational framework in the analysis of line and scatter plots. Candidates should be studying Computer Science, Electrical Engineering or related fields, with experience and interest in deep learning, image processing and — ideally — pattern recognition.

Z-2019-9

Project 9: Histopathology image analysis

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Department:   Cognitive Computing & Industry Solutions
Mentor/Manager:  Dr. Maria Gabrani

Brief description:  
Pathology image In digital pathology, we focus on the analysis of digitized histopathology and molecular expression images, as well as cytology images. Imaging of tissue specimens is a powerful tool to extract quantitative metrics of phenotypic properties while preserving the morphology and spatial relationship of the tissue microenvironment. Novel staining technologies like immunohistochemistry (IHC) and in situ hybridization (ISH) further empower the evidencing of molecular expression patterns by multicolor visualization. Such techniques are thus commonly used for predicting disease susceptibility as well as for stratification and treatment selection and monitoring. However, translating molecular expression imaging into direct health benefits has been slow, which can be attributed to two major factors. On the one hand, disease susceptibility and progression is a complex, multifactorial molecular process. Diseases such as cancer exhibit tissue and cell heterogeneity, impeding our ability to differentiate between various stages or types of cell formations, most prominently between inflammatory response and malignant cell transition. On the other hand, the relative quantification of the stained tissue selected features is ambiguous, tedious and thus time-consuming and prone to clerical error, leading to intra- and interobserver variability and low throughput. At IBM Research – Zurich, we are developing advanced image analytics to address both the above limitations, aiming to transform the analysis of stained tissue images into a high-throughput, robust, quantitative and data-driven yet explainable science.

For our growth area in digital pathology, we are looking for motivated candidates to enhance and advance our computational framework. Candidates should be studying Computer Science, Electrical Engineering or related fields, with experience and interest in deep learning, image processing and pattern recognition.

Z-2019-10

Project 10: Analysis of molecular and clinical data to integrate disparate types of data into models that can help risk-stratify patients

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Department:   Cognitive Computing & Industry Solutions
Mentor/Manager:   María Rodríguez Martínez

Brief description:  
Despite their great promise, high-throughput technologies in cancer research have often failed to translate into major therapeutic advances in the clinical environment. One challenge lies in the high level of tumour heterogeneity displayed by human cancers, which renders the identification of driving molecular alterations difficult, and thus often results in therapies that only target subsets of aggressive tumour cells. Another challenge lies in the difficulty of integrating disparate types of molecular data into mathematical disease models that can yield actionable clinical statements.

The Computational Systems Biology group at IBM Research – Zurich aims to develop new mathematical and computational approaches to analyze and exploit the latest generation of biomedical data. In the context of cancer, our group focuses on integrating high-throughput molecular datasets to build comprehensive molecular disease models, developing new approaches to reconstruct signaling protein networks from single-cell time-series proteomic data, and applying Bayesian approaches and high-performance computing to the problem of network reconstruction. An active line of research focuses on prostate cancer, a leading cause of cancer death amongst men in Europe, but also prone to over-treatment.

This internship will focus on the analysis of molecular (genomic, transcriptomic, and proteomic) and clinical data, and the use of the latest-generation cognitive technologies developed at IBM with the goal of integrating disparate types of data into models that can help risk-stratify patients. Candidates should have a strong background in computer science, machine learning, mathematics or physics and be interested in cancer-related research.

Required expertise:

  • Working knowledge of C or C++.
  • Working knowledge of Matlab, R or equivalent.
  • Comfortable knowledge of statistics and mathematical modeling.
  • Some knowledge of molecular biology, genetic and systems biology, as well as high-throughput technologies for the molecular characterization of cancer samples would be beneficial, but not essential.