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Job ID: 260933

INESC TEC | Research Grant (AE2025-0302)
INESC TEC

Research Opportunities

Telecommunications

Work description

Study and analyze the state of the art in wireless networks, particularly in extreme environments. Collaborate in the writing of technical reports on the models, mechanisms, or algorithms developed. Contribute to the development of new communications solutions for extreme environments. Write the research grant activity report.

Academic Qualifications

Master in Electrical Engineering.

Minimum profile required

Solid knowledge of the TCP/IP stack and Linux. Proven experience with open-source 5G implementations and programming in C++ and/or Python. Research and development experience in the field of wireless networks.

Preference factors

Knowledge of wireless networks. Familiarity with open-source 5G implementations.

Maintenance stipend: € 1309.64, according to the table of monthly maintenance stipend for

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RSS Feed Source: Academic Keys

Job ID: 260937

INESC TEC | RESEARCHER (AE2025-0353)
INESC TEC

Research Opportunities

Technology Management and Innovation

Work description

Participate in national and European R&D projects, developing activities for the transfer and enhancement of knowledge, tools, methodologies and technology.

Academic Qualifications

Enrolled in a master’s degree in engineering, management, economics.

Minimum profile required

Minimum 1 year’s experience.

Preference factors

Training in innovation, entrepreneurship or similar. Professional experience in the field of innovation or entrepreneurship.

Application Period

Since 07 Aug 2025 to 22 Aug 2025

Centre

Innovation, Technology and Entrepreneurship

Scientific Advisor

Alexandra Xavier

What we offer

Multicultural and collaborative environment A multicultural, international and collaborative environment that makes it easier to exchange ideas, work in networks and create synergies. International projects The possibility of working in

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Job ID: 260935

INESC TEC | Research Grant (AE2025-0351)
INESC TEC

Research Opportunities

Computer Science

Work description

Responsibilities under the grant:

Design a system for managing the energy consumption of GPUs for LLMs. Implement and optimize a prototype based on the initial design. Conduct experimental evaluations of the developed prototype, using a variety of models and hardware devices (e.g., various processing and storage devices).

The tasks described in this work plan require the application and development of concepts and techniques from Computer Engineering, which are typically addressed in the core curriculum of the Integrated Master’s Degree in Computer Engineering or the Master’s Degree in Computer Engineering.

Academic Qualifications

BSc Degree in Informatics Engineering Sciences.

Minimum profile required

Solid knowledge of energy monitoring and energy control systems (i.e.,

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The U.S. National Science Foundation Directorate for Technology, Innovation and Partnerships (NSF TIP) announced an inaugural investment of nearly $32 million to five teams across the U.S. through the NSF Use-Inspired Acceleration of Protein Design (NSF USPRD) initiative. This effort aims to accelerate the translation of artificial intelligence-based approaches to protein design and enable new applications of importance to the U.S. bioeconomy.

“NSF is pleased to bring together experts from both industry and academia to confront and overcome barriers to the widespread adoption of AI-enabled protein design,” said Erwin Gianchandani, NSF assistant director for TIP. “Each of the five awardees will focus on developing novel approaches to translate protein design techniques into practical, market-ready solutions. These efforts aim to unlock new uses for this technology in biomanufacturing, advanced materials, and other critical industries. Simply put, NSF USPRD represents a strategic investment in maintaining American leadership in biotechnology at a time of intense global competition.”

Researchers have made significant progress in predicting the 3D structures of proteins and are now leveraging this knowledge to design proteins with specific, desirable characteristics. These advances have been driven by macromolecular modeling, access to training data, applications of AI and machine learning, and high-throughput methods for protein characterization. The NSF USPRD investment seeks to build on this foundation by bringing together cross-disciplinary and cross-sector experts nationwide. The goal is to

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