Artificial Intelligence Platform for Investment Decisions in Nano and Quantum Startups
We are looking for motivated students preferable with a background in one or more of the following areas: logic, semantic technologies, ontology, reasoning, machine learning, modelling, database theory and programming languages. Experience with Python and R is an advantage.
Nordic Quantum Computing Group AS (NQCG) is a research organization that has been tracking nanotechnology and quantum technologies for nearly two decades. One of the group’s objectives is to build a business intelligence platform to find and assess startups in emerging technologies (scouting process). One of the basic building blocks of this platform shall be a tool to make the task of skimming through large bodies of information less time consuming and more rewarding.
The main objective of the MSc project is to extend the state of the art in using explicitly big-data driven insights and machine learning to support the quantum computing startup deal diligence cycle:
- Develop and exploit data analytics and business intelligence tools to improve investment decisions in emerging nanotechnology and quantum technologies domains
- Design datasets and apply machine learning techniques and methodologies for automation of selection and assessment of futuristic technologies and disruptive technology companies
- Use algorithms to identify investment opportunities earlier, i.e., apply algorithmic systems which will identify X number of investment opportunities as they emerge
This proposal encourages you to use natural language processing and artificial intelligence techniques to create a meta index of a corpus of documents, with the ultimate goal to create a tool that assists technology scouts in tasks such as:
- Finding relevant documents
- Scoring documents
- Tagging and classifying existing and new documents
- Finding documents related to or similar to a specific document
You will be using a dataset of research documents from arXiv.
Subtasks of this challenge will involve machine learning algorithms and training models. You will need training data for this. Training data is typically a tuple of input data (a document or an abstract of a document) and output data (a vector representing a number of scores that a real person rated the input data with). We will provide you with this.
We will also provide a quantum technology ontology, which is a kind of a structured vocabulary of terms used in quantum computing. You can use the ontology for simple processing steps such as finding key words to complex tasks such as answering questions using a reasoning engine.
We suggest looking at similar work done on the same corpus of documents such as: http://www.arxiv-sanity.com
We are based in Oslo, located at Fornebu, working out of the innovation hub The Simula Garage. You will be working together with our core team of software developers and collaborate with relevant key experts from Simula Research Laboratory.
Through our technology partnership with the market leader in enterprise application software, SAP, we explore joint projects in SIRIUS. You will collaborate with experts in the SAP laboratory operated in conjunction with SIRIUS, a Norwegian Centre for Research- driven Innovation.
For the right candidates we might find possibilities for summer internship.
Details and further specifications of the project can be discussed with Prof Olaf Owe (PSY) and the program manager for this project (http://nqcg.com/).
Prof Olaf Owe firstname.lastname@example.org
UiO, Reliable Systems (PSY)
Founder & CEO Axel P Mustad email@example.com
Nordic Quantum Computing Group AS (NQCG)