CAREERS

THE ROLE: DATA SCIENTIST
You will work in collaboration with our development team and utilise your analytical, statistical and programming skills to collect, analyse and interpret large datasets. You’ll have the opportunity to collaborate with a team of project managers, software engineers and other data scientists in order to build innovative AI-based products. You will develop & investigate hypotheses, structure experiments for data collection, build mathematical models and develop machine learning algorithms that will help our products to become a global success story. The role will require regular visits to our national and international project partners. Qualified candidates are expected to have passion for data science and machine learning. It is also expected that they will be effective team players with a can-do mentality, able to cope with the challenges of delivering large-scale complex projects.

REQUIRED SKILLS

• Rigorous analysis of spatial and temporal patterns
• Robust classification of objects with a scene
• Light architecture to be used in embedded systems
• Friendly interface to train the model for various applications

DESIRED SKILLS

• MSc /PhD in Computer Science, Engineering or relevant field
• Research experience will be an advantage.
• Acquisitive Learner – resourceful in acquiring and applying new knowledge
• Understanding of electronics, hardware implementations, and PCB design
• Excellent analytical and problem-solving skills
  

BENEFITS

• Full time position
• Competitive salary based on experience
• Be part and gain experience in a very fast growing industry
• Excellent career prospects
• Pleasing and flexible startup environment

PhD Candidate 1 will be recruited by University of ESSEX, United Kingdom. The thesis entitled Design of Semantic encoder and decoder is to design Semantic (Source) Encoder that detects and extracts semantic content (e.g., meaning) of the source signal and compresses or removes the irrelevant information.

PhD Candidate 2 will be recruited by University of ESSEX, United Kingdom. The thesis entitled Optimal power splitting for simultaneous power and information transfer to analyze the power splitting factor for achieving the overall communication performance of the machine-to-machine network and the implementation issues within the transceiver of the sensor nodes.

PhD Candidate 3 will be recruited by KTH, Sweden. The thesis entitled Joint optimized Shannon and Semantic encoder and decoder is to use semantic entropy for designing efficiency semantic encoder and decoder, which is defined based on the specific semantic communication scenario.

PhD Candidate 4 will be recruited by KTH, Sweden. The thesis entitled Digital Twin modelling for M2M use case is to investigate the modelling of Virtual Entity, Phyical Entity, connection and service system for Machine to machine by using graph model.

PhD Candidate 5 will be recruited by Eindhoven University of Technology, Netherlands. The thesis entitled Efficient Reconfigurable Mechanisms for Fault Recovery to design an event-driven approach to classify different faults and to activate a cooperative communication based M2M connectivity recovery mechanism.

PhD Candidate 6 will be recruited by Izmir Institute of Technology, Türkiye. The thesis entitled AoI approaches for Real-time Network Malfunction Forecast and Detection is to develop novel compression, pruning, and simplification methods to significantly decrease the complexity and size of Age of Information (AoI) models for the traffic between machines and the base station, exploiting the power of convolutional neural network.

PhD Candidate 7 will be recruited by Huawei, Germany. The thesis entitled Cyber-Physical System (CPS) Requirement Analysis and System Architecture Design is to design and develop a novel framework that allows the distribution of intelligence to optimize network functionalities from the physical to the application layer by AI/ML tools. 

PhD Candidate 8 will be recruited by Data Magnum Ltd., Portugal. The thesis entitled Blockchain modelling and optimization for Intelligent Digital Twin is to develop and implement a novel blockchain-based framework for Intelligent Digital Twin by ensuring transparency, decentralized data storage, data immutability, and machine-to-machine communication in industrial sectors.

PhD Candidate 9 will be recruited by Data Magnum Ltd., Portugal. The thesis entitled Transfer learning to Forecast Faults based on Knowledge from Existing Network is to exploit the fault classification knowledge from the rich existing network management and develop a deep convolutional transfer learning-based method for fault classification.

PhD Candidate 10 will be recruited by IRIS Automation Ltd., United Kingdom. The thesis entitled AI-based Intelligent center and decision modeling and optimization is to design AI-empowered 6G cellular network architecture in which an AI controller will act as an application on top of network operating or an independent network entity and communicate with RAN, core network, or global software defined network (SDN) controllers using open interfaces.